Journal of Social Issues, Vol. 58, No. 1, 2002, pp. 49--74

Internet Paradox Revisited
Robert Kraut,* Sara Kiesler, Bonka Boneva, Jonathon Cummings,
Vicki Helgeson, and Anne Crawford
Carnegie Mellon University

Kraut et al. (1998) reported negative effects of using the Internet on social involvement and psychological well-being among new Internet users in 1995–96.
We called the effects a “paradox” because participants used the Internet heavily
for communication, which generally has positive effects. A 3-year follow-up of 208of these respondents found that negative effects dissipated. We also report findings from a longitudinal survey in 1998–99 of 406 new computer and televisionpurchasers. This sample generally experienced positive effects of using the Internet on communication, social involvement, and well-being. However, consistent
with a “rich get richer” model, using the Internet predicted better outcomes for
extraverts and those with more social support but worse outcomes for introverts
and those with less support.
With the rapidly expanding reach of the Internet into everyday life, it is important to understand its social impact. One reason to expect significant social impact isthe Internet’s role in communication. From the early days of networked mainframe
computers to the present, interpersonal communication has been the technology’s
most frequent use (Sproull & Kiesler, 1991). Over 90% of people who used theInternet during a typical day in 2000 sent or received e-mail (Pew Internet Report,
2000), far more than used any other on-line application or information source.
Using e-mail leads people to spend more time on-line and discourages them from∗ Correspondence concerning this article should be addressed to Robert Kraut, Human-Computer
Interaction Institute, Carnegie Mellon University, 500 Forbes Avenue, Pittsburgh, PA 15213 [e-mail:robert.kraut@andrew.cmu.edu]. This research was funded by the National Science Foundation (Grants
IRI-9408271 and 9900449). In addition, initial data collection was supported through grants from Apple
Computer Inc., AT&T Research, Bell Atlantic, Bellcore, CNET, Intel Corporation, Interval Research
Corporation, Hewlett Packard Corporation, Lotus Development Corporation, the Markle Foundation,
the NPD Group, Nippon Telegraph and Telephone Corporation, Panasonic Technologies, the U.S.
Postal Service, and US West Advanced Technologies. Tridas Mukhopadhyay and William Scherlis
participated in designing and carrying out the original HomeNet studies.
49
C 2002 The Society for the Psychological Study of Social Issues

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Kraut et al.

dropping Internet service (Kraut, Mukhopadhyay, Szczypula, Kiesler, & Scherlis,
2000). Other Internet communication services are increasingly popular—instantmessaging, chat rooms, multiuser games, auctions, and myriad groups comprising“virtual social capital” on the Internet (Putnam, 2000, p. 170).
If communication dominates Internet use for a majority of its users, there isgood reason to expect that the Internet will have positive social impact. Communication, including contact with neighbors, friends, and family, and participation insocial groups improves people’s level of social support, their probability of having
fulfilling personal relationships, their sense of meaning in life, their self-esteem,
their commitment to social norms and to their communities, and their psychological and physical well-being (e.g., S. Cohen & Wills, 1985; Diener, Suh, Lucas, &Smith, 1999; Thoits, 1983; Williams, Ware, & Donald, 1981).
Through its use for communication, the Internet could have important positivesocial effects on individuals (e.g., McKenna & Bargh, 2000; McKenna, Green, &Gleason, this issue), groups and organizations (e.g., Sproull & Kiesler, 1991),
communities (e.g., Borgida et al., this issue; Wellman, Quan, Witte, & Hampton,2001), and society at large (e.g., Hiltz & Turoff, 1978). Because the Internet
permits social contact across time, distance, and personal circumstances, it allows
people to connect with distant as well as local family and friends, with coworkers,
with business contacts, and with strangers who share similar interests. Broad social
access could increase people’s social involvement, as the telephone did in an earlier
time (e.g., Fischer, 1992). It also could facilitate the formation of new relationships(Parks & Roberts, 1998), social identity and commitment among otherwise isolated
persons (McKenna & Bargh, 1998), and participation in groups and organizations
by distant or marginal members (Sproull & Kiesler, 1991).
Whether the Internet will have positive or negative social impact, however,may depend upon the quality of people’s on-line relationships and upon what
people give up to spend time on-line. Stronger social ties generally lead to bettersocial outcomes than do weaker ties (e.g., Wellman & Wortley, 1990). Many writers
have worried that the ease of Internet communication might encourage people
to spend more time alone, talking on-line with strangers or forming superficial“drive by” relationships, at the expense of deeper discussion and companionshipwith friends and family (e.g., Putnam, 2000, p. 179). Further, even if people use
the Internet to talk with close friends and family, these on-line discussions might
displace higher quality face-to-face and telephone conversation (e.g., Cummings,
Butler & Kraut, in press; Thompson & Nadler, this issue).
Research has not yet led to consensus on either the nature of social interaction on-line or its effects on social involvement and personal well-being. Somesurvey research indicates that on-line social relationships are weaker than off-line
relationships (Parks & Roberts, 1998), that people who use e-mail regard it asless valuable than other modes of communication for maintaining social relationships (Cummings et al., in press; Kraut & Attewell, 1997), that people who use

Internet Paradox Revisited51

e-mail heavily have weaker social relationships than those who do not (Riphagen &Kanfer, 1997) and that people who use the Internet heavily report spending lesstime communicating with their families (Cole, 2000). In contrast, other survey
research shows that people who use the Internet heavily report more social supportand more in-person visits with family and friends than those who use it less (Pew
Internet and American Life Project, 2000). Because this research has been conducted with different samples in different years, it is difficult to identify central
tendencies and changes in these tendencies with time. Further, the cross-sectional
nature of the research makes it impossible to distinguish self-selection (in whichsocially engaged and disengaged people use the Internet differently) from causation (in which use of the Internet encourages or discourages social engagement).
A longitudinal study by Kraut and his colleagues (1998) was one of the firstto assess the causal direction of the relationship between Internet use and social
involvement and psychological well-being. The HomeNet field trial followed
93 households in their first 12–18 months on-line. The authors had predictedthat the Internet would increase users’ social networks and the amount of social
support to which they had access. The consequence should be that heavy Internet
users would be less lonely, have better mental health, and be less harmed by thestressful life events they experienced (S. Cohen & Wills, 1985). The sample as awhole reported high well-being at the start of the study. Contrary to predictions,however, the association of Internet use with changes in the social and psychological variables showed that participants who used the Internet more heavily becameless socially involved and more lonely than light users and reported an increase indepressive symptoms. These changes occurred even though participants’ dominant
use of the Internet was communication.
These findings were controversial. Some critics argued that because the research design did not include a control group without access to the Internet, external
events or statistical regression could have been responsible for participants’ declines in social involvement and psychological well-being (e.g., Gross, Juvonen, &Gable, this issue; Shapiro, 1999). However, these factors would have affected heavy
and light Internet users similarly, so they could not account for the differences inoutcomes between them.
A more pertinent problem noted in the original HomeNet report is the unknown
generalizability of the results over people and time. The participants in the original
study were an opportunity sample of families in Pittsburgh. In 1995 and 1996, when
they began the study, they initially had higher community involvement and more social ties than the population at large. In addition, they had little experience on-line,
and few of their family and friends had Internet access. One possibility is thatusing the Internet disrupted this group’s existing social relationships. Had thestudy begun with a more socially deprived sample or more recently, when moreof the population was on-line, the group’s use of the Internet for social interaction
might have led to more positive effects. In addition, some critics questioned the52

Kraut et al.

particular measures of social involvement and well-being deployed in this study(e.g., Shapiro, 1999).
The present article addresses these issues of generalizability through afollow-up of the original HomeNet sample and a new longitudinal study. The
rationale for both studies is similar. If use of the Internet changes the amount andtype of interpersonal communication people engage in and the connections they
have to their friends, family, and communities, then it should also influence avariety of psychological outcomes, including their emotions, self-esteem, depressive symptoms, and reactions to stressors (e.g., S. Cohen & Wills, 1985; Diener
et al., 1999; Thoits, 1983; Williams et al., 1981). The follow-up study examined
the longer-term impact of Internet use on those in the original HomeNet sample,
providing a second look at a group for whom initial Internet use had poor effects.
It retained the outcome measures collected in the original HomeNet study.
A second study followed a new sample in the Pittsburgh area, from 1998 and1999. It compared an explicit control group of those who had recently purchased a
television set with those who had recently purchased a computer. It examined theimpact of the Internet on a broader variety of social and psychological outcome
measures than did the original HomeNet study. The goal was not to make differentiated predictions for each measure, but to see if using the Internet had similarconsequences across a variety of measures of social involvement and psychological well-being. The sample was sufficiently large to permit an analysis of the
impact of individual differences in personality and social resources on Internet usage and outcomes. In particular, the research examined whether using the Internet
had different consequences for people differing in extraversion and in social support. As discussed further in the introduction to Study 2, people differing in extraversion and social support are likely use the Internet in different ways. In addition, they are likely to have different social resources available in their off-line
lives, which could change the benefits they might gain from social resources theyacquire on-line.
Study 1: Follow-Up to the Original HomeNet Sample
The data for the follow-up study are from 208 members of 93 Pittsburgh
families to whom we provided a computer and access to the Internet in 1995 or
1996. The families were recruited through four high school journalism programs
and four community development organizations in eight Pittsburgh neighborhoods.
The sample was more demographically diverse than was typical of Internet users
at the time. Details of the sampling and research protocol are described in Kraut,
Scherlis, Mukhopadhyay, Manning, and Kiesler (1996).
The analyses of social impact reported in Kraut et al. (1998) were drawn fromInternet usage records and from surveys given just before participants began the
study and again in May 1997. Server software recorded participants’ use of theInternet Paradox Revisited

53

Internet—hours on-line, e-mail volume, and Web sites visited per week. The surveys included four measures of social involvement (time spent in family communication, size of local social network, size of distant social network, and perceived
social support; S. Cohen, Mermelstein, Kamarck, & Hoberman, 1984), and threewell-established measures of psychological well-being: the UCLA Loneliness
Scale (Russell, Peplau, & Cutrona, 1980), the Daily Life Hassles Scale, a measureof daily-life stress (Kanner, Coyne, Schaefer, & Lazarus, 1981), and the Center for
Epidemiological Studies’ Depression Scale (CES-D; Radloff, 1977). It included
the demographic characteristics of age, gender, household income, and race ascontrol variables, because there is evidence that these factors influence both theamount of Internet use and its social and psychological outcomes (e.g., Magnus,
Diener, Fujita, & Payot, 1993; Von Dras & Siegler, 1997). We also included thepersonality trait of extraversion (Bendig, 1962) as a control variable, because extraversion is often associated with well-being (Diener et al., 1999) and may also
influence the way people use the Internet. However, the sample was too small to
examine statistical interactions involving the extraversion measure. See Table 1
for basic statistics and other information about these variables.
Kraut et al. (1998) used a regression analysis of the effect of hours of Internet use on social involvement and psychological well-being in 1997 (Time 2),
controlling for scores on these outcome measures at the pretest (Time 1) and thedemographic and personality control variables. The follow-up study reexamined
the impact of use of the Internet by adding a third survey, administered in February
1998 (Time 3). For about half the participants, the final survey came nearly 3 years
after they first used the Internet; for the other half, the final survey came nearly
2 years later.
MethodAll longitudinal research faces the potential of participant attrition. Our research was especially vulnerable because we had not planned initially to follow
the participants for more than 1 year. Many of the high school students in the original sample graduated and moved to college. Further, technology changed rapidly
during this period, and some participants changed Internet providers, ending ourability to monitor their Internet use. Of the 335 people who qualified for participation in the original study, 261 returned a pretest survey at Time 1 (78%), 227
returned a survey at Time 2 (68%), and 154 returned a survey at Time 3 (46%).
Because this research is fundamentally about changes in social and psychologicaloutcomes, we limit analysis to 208 participants who completed a minimum of two
out of three surveys.
We used a longitudinal panel design to examine the variables that influenced
changes in social involvement and psychological well-being from Time 1 to Time 2
and from Time 2 to Time 3. The measure of Internet use is the average hours per

54

Kraut et al.
Table 1. Descriptive Statistics for Variables in Studies 1 and 2
Study 1

Study 2

Variable

Mean

SD

N

Alpha

Mean

SD

N

Adulta
Malea
WhiteaIncomeb
Educationc
Computer samplea
Extraversiong
Social supportg
Internet useh
Local circle (log)dDistant circle (log)e
Family communication (log)f
Face-to-face communicationh
Phone communicationg
Closeness near friendsg
Closeness distant friendsg
Community involvementg
Stay in Pittsburghg
TrustgAnomieg
Stressj
Lonelinessg
Depressioni
Negative affectg
Positive affectg
Time pressureg
Self-esteemg
Computer skillg
US knowledgek
Local knowledgek

.66
.42
.72
5.53

.48
.50
.45
1.27

208
208
208
197

3.54
4.02
.72
3.01
3.01
4.31

.77
.57
.76
.81
1.15
.78

204
206
206
206
206
193

.24
1.93
.65

.17
.68
.40

208
204
205

NA
NA
NA
NA
NA
NA
.80
.81
.86
NA
NA
NA
.55
.83
NA
NA
.70
NA
.74
.57
.88
.75
.88
.88
.88
.82
.85
.90
.41
.34

.88
.47
.92
4.91
4.06
.72
3.22
3.80
.00
2.56
2.21
4.10
−.01
4.69
3.54
2.94
2.83
3.69
3.17
2.66
.22
2.10
.53
1.67
3.49
3.02
3.70
3.26
.71
.68

.32
.50
.27
1.55
1.23
.45
.65
.54
.78
.79
1.05
1.63
1.00
1.15
.76
1.10
.75
1.38
.83
.63
.14
.66
.47
.64
.72
.76
.62
.93
.33
.26

446
446
438
443
446
446
389
389
406
375
361
389
406
387
434
286
390
388
391
391
382
389
389
390
388
390
389
389
388
388

Note: All variables are coded so that higher numbers indicate more of the variable. NA = not available.
aDichotomous variable (0/1). b Six categories, from under $10,000 to over $75,000. c Six categories,
from less than 11th grade to graduate-level work. d Truncated at 60 and logged. e Truncated at 100
and logged. f Sum of minutes communicating with other household members, logged. g 5-point Likert
response scale, with endpoints 1 and 5, where 5 is highest score. h Hours per week using the Internet(logged) in Study 1; mean of standardized variables in study. i 4-point Likert scale, with endpoints 0
and 3, where 3 is highest score. j Mean of dichotomous response scales (0/1). k Proportion correct on
multiple choice questions.

week a participant spent on-line between any two surveys, according to automatedusage records (i.e., weekly use between Times 1 and 2 and between Times 2
and 3). Because this variable was highly skewed, we used a log transformation.
When assessing the impact of Internet use on social involvement and psychological well-being at one time, we statistically controlled for the prior level of social
involvement and psychological well-being, by including the lagged dependent
variable as a control variable in the model. Since this analysis controls for participants’ demographic characteristics and the lagged outcome, one can interpret theInternet Paradox Revisited55

coefficients associated with Internet use as the effect of Internet use on changes
in these outcomes (J. Cohen & Cohen, 1983, pp. 417–422). (For example, when
examining the effect of Internet use on loneliness at Times 2 and 3, we included thelagged variable for loneliness at Times 1 and 2, respectively, in the model to control
for the effects of prior loneliness on Internet use and on subsequent loneliness.)
As demographic control variables, we included adult status (0 if age ≤ 18; 1
if age > 18), gender (0 = female; 1 = male), race (0 = non-White; 1 = White) andhousehold income. Because teens use the Internet substantially more than adults
and in different ways (Kraut et al., 1998), we included the Generation × Internet
Use interaction to determine whether the Internet had similar effects on bothgenerations. Because the personality trait of extraversion is likely to influencesocial involvement, Bendig’s (1962) measure of extraversion was included as acontrol variable when we were predicting social support and the size of local anddistant social circles. Because daily-life stress is a risk factor for psychologicaldepression, we included Kanner et al.’s (1981) hassles scale as a control variable
when predicting depressive symptoms.
The analyses were conducted using the xtreg procedure in Stata (StataCorp,
2001) for cross-sectional time series analyses with independent variables modeled
as a fixed effect and participant modeled as a random effect. For the dependentmeasures listed in Table 2, the basic model is
Dependent VariableTn = Intercept + Demographic CharacteristicsT1+ Time Period + Dependent VariableTn−1+ Control VariablesTn + Log Internet HoursTn−1+ Log Internet HoursTn−1 × Time Period+ Log Internet HoursTn−1 ×GenerationT1 .
In the model Dependent VariableTn is a measure of social involvement or psychological well-being at the end of the second or third time period and Dependent
VariableTn−1 represents the same measure administered in the previous time period. The analyses of particular interest are the main effects of Internet use on
subsequent measures of social involvement and psychological well-being and thestatistical interactions of Internet use and time period on these outcomes. The main
effect of Internet use assesses the cumulative impact of Internet use over the twoor three years of the study, and the interaction of Internet use with time period
assesses whether this impact is the same in the early period (previously reported
in Kraut et al., 1998) and in the more recent period.
Results
Table 2 shows results from the analyses. Kraut et al. (1998) showed Internet use
was associated with declines in family communication and in the number of peopleTable 2. Analysis of the Original HomeNet Study After 3 Years
Social supportapLocal social circleb

Distant social circlec

beta

SE

p

beta

SE

3.76
−19.37

3.37
7.41

∗∗

8.85
−49.02

6.74
14.70

−2.74

6.89

6.57

Independent variables

beta

SE

Intercept
Adult (0 = teen;
1 = adult)
Male (0 = female;
1 = male)Household income
White (0 = other;
1 = White)
Time periodh
StresseExtraversioni
Lagged dependent
variable j
Internet hours (log)Internet × Period
Internet × Adult
n
R2

0.00
−0.13

0.04
0.09

−0.16

0.08

0.00
0.15

0.00
0.09

−0.20
−8.26

0.15
8.23

0.10

0.06

0.97

2.52

0.07
0.45

0.05
0.07

1.04
0.21

2.74
0.06

0.02
0.10
0.06
189
0.29

0.05
0.08
0.09

−1.15
−0.37
5.44
189
0.26

3.29
3.06
6.08

∗

∗∗∗

∗∗∗

Familycommunication (log)d

p

beta

SE

∗∗∗

−0.03
0.34

0.05
0.11

13.70

−0.08

0.14
−6.74

0.29
16.38

−4.04

4.66

−5.28
0.33

5.21
0.10

−5.14
2.88
7.52
187
0.17

6.27
5.62
11.57

∗∗∗

StresseDepressionfpLonelinessg

p

beta

SE

beta

SE

∗∗

0.01
0.00

0.01
0.02

−0.01
−0.14

0.03
0.06

0.10

0.00

0.02

0.02

0.05

0.00
0.11

0.00
0.13

0.00
0.04

0.00
0.02

†

0.00
−0.14

0.00
0.07

∗

−0.34

0.10

∗∗∗

0.06

0.01

∗∗∗

0.01
0.61

0.04
0.17

∗∗∗

0.37

0.08

∗∗∗

0.54

0.06

∗∗∗

0.18

0.06

∗∗∗

0.05
0.16
−0.02
177
0.15

0.07
0.12
0.13

0.03
−0.01
0.04
195
0.46

0.01
0.02
0.02

∗

−0.01
−0.13
−0.08
187
0.20

0.03
0.05
0.06

∗

†

p

beta

SE

∗

0.03
0.04

0.04
0.09

0.27

0.08

∗∗

0.00
−0.22

0.00
0.10

∗

0.12

0.06

†

0.44

0.05

∗∗∗

0.00
−0.21
−0.09
186
0.36

0.05
0.08
0.10

∗

p

∗∗

Note. Variables were centered before analyses. n = 208.
a
Cohen, Mermelstein, Kamarck, & Hoberman, 1984. b Number kept up with monthly, living in the Pittsburgh area. c Number kept up with annually, living outside of
the Pittsburgh area. d Log of the minutes communicating per day. e Kanner, Coyne, Schaefer, & Lazarus, 1981. f Radloff, 1977. g Russell, Peplau, & Cutrona, 1980.
hPeriod 1 is 12–18 months, from 1995 or 1996 to 1997, and period 2 is from the first posttest in 1997 to the second posttest in 1998. i Bendig, 1962. j Dependent
variable measured approximately 12–18 months previously.
†
p < .10. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

Internet Paradox Revisited57

in participants’ local and distant social circles, and with increases in loneliness,depressive symptoms, and daily-life stress. Of these effects, Internet use over the
longer period tested in the current analyses was associated only with increases
in stress. Two significant Internet Use × Time Period interactions suggest that
Internet use had different effects early and late in respondents’ use of the Internet.
In particular, depressive symptoms significantly increased with Internet use during
the first period but significantly declined with Internet use during the second period(for the interaction, p < .05). Loneliness significantly increased with Internet use
during the first period but was not associated with Internet use during the second
period (for the interaction, p < .01). Whether these differences in results over timereflect participants’ learning how to use the Internet as they gain more experienceor whether they reflect changes in the Internet itself over this period is a topic wewill return to in the Discussion.
Because teenagers use the Internet more than their parents and because teens
and adults differed on several of the outcomes reported in Table 2, we tested
the differential effects of Internet use with age. There was only one marginallysignificant interaction: Adults’ stress increased more than teens’ stress with moreInternet use (p < .10).
Study 2: A Longitudinal Study of Computer and Television Purchasers
Study 2 is a replication of the original HomeNet research design in a sample ofhouseholds that had recently purchased new home technology: either a computeror TV. We added controls to the design and new measures. First, we attempted
to manipulate Internet use to create a true experiment, with participants randomly
assigned to condition. We recruited households who recently bought a new homecomputer and randomly offered half free Internet service; households in the control
condition received an equivalent amount of money ($225) to participate. Unfortunately, this experimental procedure failed when, by the end of 12 months, 83%of the control households obtained Internet access on their own (versus 95% of
the experimental households who took advantage of free Internet service). Because this attempt to conduct a true experiment failed, we combined the groups
for analyses of the effects of using the Internet.
Another design change was to add a comparison group: recent purchasersof a new television set. Study 1 had only compared heavier and lighter users of
the Internet, all of whom had access to it. The addition of a television purchaser
comparison group in Study 2 (of whom just 29% obtained Internet access after
12 months) provided a sample that was unlikely to use the Internet and helped
to rule out explanations of change based on external events. In analyses of theeffects of Internet use, we included participants from the television purchaser groupbut controlled for sample selection bias by creating a dummy variable indicating
whether participants were recruited for buying a television or computer.

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Kraut et al.

We also increased the number of dependent variables to examine the generalizability of the effects of using the Internet across outcomes and measures. The
original study contained four measures of personal social involvement and threeof psychological well-being. We added measures of personal social involvement(spending time with family and friends, use of the telephone, perceived closeness toa random sample from the respondents’ social networks). In response to Putnam’s(2000) concerns that the Internet might undercut community participation as wellas interpersonal contact, we added measures of involvement with and attitudes
toward the community at large. To measure psychological well-being, we added
scales measuring the experience of negative and positive affect, perceived timepressure, and self-esteem. Because the Internet is a source of information as well
as social contact, we added knowledge tests and a scale to measure computing
skill. To test whether the distance-minimizing properties of the Internet blur traditional distinctions between geographically close and distant regions, our measuresof social involvement and knowledge differentiated between these, for example,
asking separately about local and distant social circles and about knowledge of thePittsburgh region and broader areas.
Finally, we extended the HomeNet study conceptually by examining the differential effects of individual differences in extraversion and perceived social support
on the effects of Internet use. Extraversion is the tendency to like people, to beoutgoing, and to enjoy social interaction; it is a highly stable personality trait,
predictive of social support, social integration, well-being, and positive life events(e.g., Magnus et al., 1993; Von Dras & Siegler, 1997). The perception of social
support refers to feelings that others are available to provide comfort, esteem, assistance, and information or advice; perceived social support buffers the effects ofstress (e.g., Cohen, 1988).
We offer two opposing models of the relationship between extraversion andsocial support and Internet use. A “rich get richer” model predicts that those whoare highly sociable and have existing social support will get more social benefit
from using the Internet. Highly sociable people may reach out to others on theInternet and be especially likely to use the Internet for communication. Those who
already have social support can use the Internet to reinforce ties with those in theirsupport networks. If so, these groups would gain more social involvement andwell-being from using the Internet than those who are introverted or have limited
networks. They can gain these benefits both by adding members to their social
networks and by strengthening existing ties.
By contrast, a “social compensation” model predicts that those who are introverted or lack social support would profit most from using the Internet. People withfewer social resources could use the new communication opportunities on-line toform connections with people and obtain supportive communications and useful
information otherwise missing locally (see McKenna & Bargh, 1998). At the sametime, for those who already have satisfactory relationships, using the Internet might

Internet Paradox Revisited

59

interfere with their real-world relationships if they swap strong real-world ties forweaker ones on-line. Analogous to the finding that cancer patients with emotionally supportive spouses can be harmed by participating in peer-discussion support
groups (Helgeson, Cohen, Schulz, & Yasko, 2000), it is possible that people withstrong local relationships might turn away from family and friends if they used theInternet for social interaction.
MethodSample. We recruited participants through advertisements placed in local
newspapers, soliciting for a study of household technology people who purchased
a new computer or new television within the previous 6 months. We obtained
agreement from all adults and children in the family above age 10 to complete
surveys. Half of the computer purchaser households were randomly offered freeInternet access to participate in the study; the other participants were offered payments to complete surveys. After the initial telephone contact, we mailed consent
forms and pretest surveys with return envelopes. Unlike the procedures used inStudy 1, we did not encourage Internet use or provide technology support.
Measures. We administered surveys three times during the study, in February
1998, 6 months later (August 1998), and 1 year later (February 1999). Because
we had automated measures of Internet usage only for the group randomly given
Internet access, our main independent variable is an index of self-reported Internet
use (e.g., “I use the World Wide Web very frequently”; “Time per day spent usinge-mail”; “Frequency per month of using a computer at home”; the full text of unpublished measures is available at http://HomeNet.hcii.cs.cmu.edu/progress/research.
html). Within the group randomly given Internet access, the Pearson correlations
between the self-report index of Internet use and the automated count of the numberof sessions logged into the Internet in the 8 weeks surrounding the questionnaires
was moderate, r (112) = .55 at Time 2 and r (104) = .42 at Time 3. These correlations reflect moderate validity of the self-report measure, although they are far
from perfect because there is error both in the self-reports and in the server data
(e.g., the usage records do not include Internet use at work and include cases inwhich one family member uses another’s account).
We used self-report measures to assess demographic characteristics of theparticipants and measures from the original HomeNet study, including perceived
social support (S. Cohen et al., 1984), size of local and distant social circles, andtime talking with other family members. We used the same measure of extraversion (Bendig, 1962). We added new measures of anomie (Srole, 1956), trust inpeople (Rosenberg, 1957, revised from Survey Research Center, 1969), community involvement (adapted from Mowday, Steers, & Porter’s [1979] measure oforganizational commitment; e.g., I spend a lot of time participating in community

60

Kraut et al.

activities; I feel part of the community in Pittsburgh), and intentions to stay in the
Pittsburgh area (Even if I had a chance to move to another city, I would very muchwant to stay in the Pittsburgh area). We also assessed respondents’ relationships
with specific family and friends by asking them “How close do you feel?” to fiveindividuals living in the Pittsburgh area and five living outside of the area who
were closest to them in age. Participants described closeness to each nominee on
5-point Likert scales.
To assess well-being, we again used the CES-D to measure depressive symptoms (Radloff, 1977), the Daily Life Hassles Scale (Kanner et al., 1981), and the
UCLA Loneliness Scale (Russell et al., 1980) from the original HomeNet study.
We added measures of self-esteem (Heatherton & Polivy, 1991), positive and negative affect (Watson, Clark, & Tellegen, 1988), perceived time pressure (adaptedfrom Kraut & Attewell, 1997) and physical health (scale from the SF-36 shortform, health survey; Ware, Snow, Kosinski, & Gandek, 1993).
Finally, because the Internet is a source of information as well as communication, we added measures of knowledge. We included a self-report measureof skill using computers, expanded from the original HomeNet study (e.g., I
am very skilled at using computers; I don’t know much about using computers [reverse-scored]). We also added a test of knowledge, including multiple
choice items on national current events, Pittsburgh current events, and generalknowledge from a high school equivalency test (Research & Education Association, 1996). The knowledge test contained different items at different time
periods.
Analyses. Data come from 216 households. Of the 446 individuals who were
eligible to be in the sample, 96% completed survey 1, 83% completed survey 2 and
83.2% completed survey 3. Analyses are based on 406 respondents (91% of theoriginal sample) who completed at least two surveys. The analyses were similar to
those for Study 1. We used Stata’s xtreg procedure, with participant as a random
effect (StataCorp, 2001), to analyze the panel design. In the Study 2 models, social
involvement, well-being, and knowledge outcomes at the second and third time
periods were regressed on self-reported Internet use during those periods, controlling for demographic characteristics and the lagged dependent variables. The
models control for whether the respondent came from the television purchaser orcomputer purchaser subsample and whether the data for the dependent variables
were collected at the second or third time period. To test whether levels of extraversion and social support moderated the effects of using the Internet, we included
the main effects for the Bendig (1962) measure of extraversion and S. Cohenet al.’s (1984) measure of social support and the interaction of these variables
with Internet use. We included adult status, gender, race, education, and household income as demographic controls. Because teenagers use the Internet quite
differently from adults, we also included the interaction of generation with Internet use.

Internet Paradox Revisited

61

Results
Table 2 shows scale reliabilities and descriptive statistics for variables in thesample, averaged over the three time periods. A table of correlations is available
at http://HomeNet.hcii.cs.cmu.edu/progress/research.html
Effects on interpersonal and community social involvement. Models testing
the effects of using the Internet on interpersonal communication and community
involvement are shown in Tables 3 and 4, respectively. The main effects of Internet
use on these measures of social involvement were generally positive. As Table 3
shows, participants who used the Internet more had larger increases in the sizesof their local social circle (p < .01 ) and distant social circle (p < .01) and their faceto-face interaction with friends and family increased (p < .05). As Table 4 shows,they also reported becoming more involved in community activities (p < .10) andfelt greater trust in people (p < .05). The only significant reversal to the positive
trend is that those who used the Internet more became less committed to living in
the Pittsburgh area (p < .05).
The interaction with extraversion shows that the association of Internet use
with changes in community involvement was positive for extraverts and negative
for introverts. Figure 1a illustrates these effects. Holding constant respondents’prior community involvement, extraverts who used the Internet extensively reported more community involvement than those who rarely used it; on the otherFig. 1. Interaction of Internet use and extraversion with community involvement and loneliness.
Note. Plots show the effects on community involvement and loneliness of Internet use for people
differing in extraversion. Plots show predictions from the models reported in Tables 4 and 5 as Internet
use and extraversion move through the range appearing in the sample. Internet use varied from 1.12
standard deviation units less than the mean to 2.54 standard deviation units greater than the mean.
The “Introvert” line represents the most introverted respondent, with an extraversion score −2.12 units
below the mean, corresponding to a value of 1.10 on the original 5-point Bendig (1962) Extraversion
Scale. The “Extravert” line represents the most extraverted respondent, with a score 1.78 units greaterthan the mean, corresponding to a value of 5 on the original scale.

Table 3. Predicting Interpersonal Social Involvement as a Function of Use of the Internet Over Time and Individual Difference Variables, Study 2
Independent
variables
Intercept
Adult (0 = teen;
1 = adult)
Male (0 = female;
1 = male)Household income
White (0 = other;
1 = White)
Education
Computer sample(0 = no; 1 = yes)
Time period (0 = 1st
6 months; 1 = 2nd
6 months)
Lagged dependent
variable
Extraversionf
Social supporta
Internet usee
Internet × Extraversion
Internet × Support
Internet × Adultn
R2Social supporta
beta

SE

−0.01 0.02
0.18 0.05p∗∗∗Local social
circle (log)b
beta

SE

p

beta

SE

−0.02 0.03
−0.04 0.10

0.01 0.04
0.31 0.12−0.08 0.07

−0.09 0.03∗∗

0.03 0.06

0.15 0.06
0.02 0.01∗

0.37 0.12−0.01 0.02∗

Distant social
circle (log)c∗∗

0.28 0.15
0.01 0.03

Familycommunication (log)dFace-to-facecommunicatione

p

beta

SE

p

beta

p

beta

∗∗∗

∗

0.29 0.01
0.00 0.03

0.02 0.03−0.55 0.11∗∗∗

−0.02 0.03
0.12 0.10−0.01 0.02

−0.19 0.07∗∗

−0.03 0.04
−0.01 0.01

−0.11 0.13
−0.01 0.02

0.00 0.01−0.01 0.02

−0.04 0.03
−0.22 0.08
0.03 0.05††

0.01 0.01
0.02 0.04

0.00 0.03
0.12 0.07

0.06 0.03
0.07 0.09

0.01 0.02−0.05 0.04

−0.12 0.05∗

0.00 0.01

0.46 0.03∗∗∗

3.86 0.04

0.53 0.03∗∗∗

0.15 0.03∗∗∗−0.01 0.02
0.01 0.03−0.11 0.06
406
.51

†

0.33 0.04
0.09
0.17
0.12
0.02
0.01
−0.13
385
.42

0.05
0.05
0.04
0.06
0.07
0.11

∗∗∗
∗
∗∗∗
∗∗

0.09
0.13
0.15
−0.05
0.02
−0.02
365
.47

0.06
0.07
0.05
0.07
0.09
0.15

†
∗∗

0.02
0.04
0.00
−0.01
0.05
−0.06
373
.95

0.01
0.02
0.01
0.02
0.02
0.03

∗∗∗

∗

∗∗

†

SE

0.28 0.03
0.14
0.28
0.09
−0.02
−0.11
0.30
406
.31

0.05
0.07
0.04
0.07
0.08
0.13

∗∗

∗∗∗
∗∗
∗∗∗
∗

∗

Phone
communicationeSEp

Closeness tolocal friendse
beta

SE

Closeness to
distant friendsep−0.01 0.06
0.27 0.17

SE
0.04
0.16

−0.29 0.12

∗

−0.02

0.09

−0.04 0.13
0.03 0.02−0.41 0.25
−0.09 0.04

†

−0.16
0.01

0.20
0.03

−0.02 0.03
−0.03 0.08

0.00 0.05−0.10 0.13

−0.01
−0.10

0.04
0.10

−0.04

0.06

0.50

0.04

0.01
0.30
0.07
0.01
0.15
0.35
285
.44

0.07
0.08
0.06
0.08
0.10
0.18

−0.30 0.07

∗∗∗

beta
−0.01
0.15

0.08 0.04†

0.00 0.00

0.50 0.03∗∗∗

0.99 0.00

∗∗

0.00
0.00
0.00
0.00
0.00
0.00
351
.16

0.16
0.11
0.05
0.10
−0.08
0.04
391
.51

0.05
0.06
0.04
0.06
0.07
0.12

†

0.00
0.00
0.00
0.00
0.00
0.00

∗

∗∗∗

p

∗∗∗

∗∗∗

∗

Note. Variables were centered before analyses.
aCohen, Mermelstein, Kamarck, & Hoberman, 1984. b Number kept up with monthly, living in the Pittsburgh area. c Number kept up with annually, living outsideof the Pittsburgh area. d Minutes communicating per day. e See Table 2. f Bendig, 1962.
† p < .10. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

Table 4. Predicting Community Social Involvement as a Function of Use of the Internet Over Time and Individual Difference Variables, Study 2
Community
involvement a
Independent variables

beta

SE

Intercept
Adult (0 = teen; 1 = adult)
Male (0 = female; 1 = male)Household income
White (0 = other; 1 = White)
Education
Computer sample (0 = no;1 = yes)
Time period (0 = 1st 6 months;
1 = 2nd 6 months)
Lagged dependent variable
Extraversiond
Social supporte
Internet used
Internet × Extraversion
Internet × Support
Internet × Adult
n
R2

0.00
0.11
−0.09
−0.10
−0.05
0.05
0.09
0.01

0.02
0.07
0.04
0.09
0.02
0.02
0.05
0.04

0.51
0.17
0.17
0.05
0.10
0.02
−0.01
403
.50

0.03
0.04
0.04
0.03
0.05
0.05
0.09

Stay inPittsburgha
p

∗
∗∗
∗∗
†

∗∗∗
∗∗∗
∗∗∗
†
∗

beta

SE

−0.02
−0.01
0.11
0.47
−0.06
0.01
0.11
−0.07

0.04
0.14
0.08
0.18
0.03
0.04
0.10
0.06

0.55
0.13
0.19
−0.13
0.09
−0.08
0.10
402
.49

0.03
0.07
0.08
0.06
0.09
0.10
0.17

Trustb
p

∗∗
∗

∗∗∗
∗
∗
∗

beta

SE

−0.01
0.30
−0.01
0.22
−0.02
0.04
0.07
−0.01

0.02
0.08
0.05
0.10
0.02
0.02
0.06
0.04

0.51
0.07
0.21
0.07
0.00
0.02
−0.12
405
.48

0.03
0.04
0.05
0.03
0.05
0.06
0.10

Note. Variables were centered before analyses.
a
See Table 2. b Srole, 1956. c Rosenberg, 1957. d Bendig, 1962. e Cohen, Mermelstein, Kamarck, & Hoberman, 1984.
†
p < .10. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

Anomiec
p
∗∗∗
∗
†

∗∗∗
†
∗∗∗
∗

beta

SE

0.00
−0.24
0.07
−0.12
−0.03
−0.03
−0.07
0.04

0.02
0.06
0.04
0.08
0.01
0.02
0.05
0.03

0.43
−0.06
−0.16
−0.01
−0.01
0.02
−0.04
405
.47

0.03
0.03
0.04
0.03
0.04
0.05
0.08

p
∗∗∗
∗
†
∗

∗∗∗
†
∗∗∗

64

Kraut et al.

hand, introverts who used the Internet extensively reported less community involvement than those who rarely used it. Interactions of Internet use with social
support show that Internet use was associated with larger increases in family
communication for those who initially had more social support. Each of theseinteraction effects supports the “rich get richer” hypothesis.
Finally, interactions of age with Internet use suggest different positive effects
for adults and teens. Teens, as compared with adults, increased their social support and family communication with more Internet use, whereas adults increased
their face-to-face interaction with family and friends and their closeness to distantrelatives and friends with more Internet use.
Effects on psychological and physical well-being. Table 5 shows the effects ofInternet use on psychological well-being. These results are mixed, showing that,
overall, both stress and positive affect increased with Internet use. The several
interactions of Internet use with extraversion indicate that Internet use was associated with better outcomes for extraverts and worse outcomes for introverts. In
particular, extraverts who used the Internet more reported increased well-being,
including decreased levels of loneliness, decreased negative affect, decreased timepressure, and increased self-esteem. In contrast, these same variables showed declines in well-being for introverts. Figure 1b illustrates these effects. Holding
constant prior loneliness, extraverts who used the Internet extensively were lesslonely than those who rarely used it, whereas introverts who used the Internet
extensively were more lonely than those who rarely used it. There were no interactions with social support or with age and no effects on measures of physical health(not shown in the table).
Effects on skill and knowledge. Table 6 shows the effects of Internet use on
self-reported computer skill and multiple choice tests of knowledge. Computer
skill increased with more Internet use (p < .001); this increase was larger among
those with more social support (p < .05). General knowledge (not shown in thetable) and knowledge of national current events did not change with Internet use.
In contrast, those who used the Internet more became less knowledgeable about
the local Pittsburgh area (p < .05).
Different uses of the Internet. Because the way people choose to use the Internet could strongly influence its effects, we asked participants to report how often
they used the Internet for various purposes. We conducted an exploratory factor
analysis of these items to create four scales reflecting different uses of the Internet:(a) for communication with friends and family; (b) for acquiring information
for school, work, news, and other instrumental purposes such as shopping; (c) for
entertainment, such as playing games, downloading music, and escape; and(d) for meeting new people and socializing in chat rooms. These uses of the Internet
were moderately interrelated (mean r = .51). Using the Internet for communication

Table 5. Predicting Psychological Well-Being as a Function of Use of the Internet Over Time and Individual Difference Variables, Study 2
Stressa
Independent variables

beta

SE

Intercept
Adult (0 = teen; 1 = adult)
Male (0 = female; 1 = male)Household income
White (0 = other; 1 = White)
Education
Computer sample (0 = no;1 = yes)Time period(0 = 1st 6 months;
1 = 2nd 6 months)
Lagged dependent variable
Extraversion
Social support
Internet use
Internet × Extraversion
Internet × Support
Internet × Adult
n
R2

0.00
0.04
−0.01
0.00
0.00
0.01
−0.02

0.00
0.02
0.01
0.02
0.00
0.00
0.01

Lonelinessb
p
∗∗

†

0.03
0.01
0.01
0.01
0.01
0.01
0.02

SE

0.00
0.08
−0.01
−0.10
−0.01
0.01
−0.06

0.02
0.06
0.03
0.07
0.01
0.02
0.04

p

−0.04 0.03

0.01 0.01
0.54
0.00
−0.02
0.01
−0.01
0.01
−0.02
398
.51

beta

Depressionc

∗∗∗
∗
∗

0.27
−0.21
−0.59
0.03
−0.08
0.01
−0.10
406
.66

0.03
0.03
0.04
0.02
0.03
0.04
0.07

beta

SE

0.01
0.01
0.02
0.01
−0.02
−0.01
−0.03

0.01
0.05
0.03
0.06
0.01
0.01
0.04

−0.04 0.02
∗∗∗
∗∗∗
∗∗∗
∗

0.48
0.03
−0.21
0.01
−0.05
0.01
−0.09
405
.48

0.03
0.02
0.03
0.02
0.03
0.04
0.06

p

†

†
∗∗∗
∗∗∗

Negative affect dPositive affect dTime pressuree

beta

SE

beta

SE

beta

SE

0.01
−0.12
−0.02
−0.03
−0.03
0.03
−0.08

0.02
0.07
0.04
0.09
0.02
0.02
0.05

0.00
0.05
0.07
−0.15
0.02
0.00
−0.02

0.02
0.08
0.05
0.09
0.02
0.02
0.06

†

0.00
0.23
−0.18
0.12
0.03
−0.02
−0.03

0.02
0.09
0.05
0.10
0.02
0.02
0.06

0.07 0.03

∗

p
†

∗

−0.04 0.03
0.39
0.01
−0.23
0.04
−0.12
−0.08
−0.13
405
.40

0.03
0.04
0.04
0.03
0.04
0.05
0.09

∗∗∗
∗∗∗
∗∗

0.32
0.09
0.41
0.14
0.04
−0.08
0.10
405
.43

0.03
0.04
0.05
0.03
0.05
0.06
0.09

p

∗∗∗
∗
∗∗∗
∗∗∗

−0.06 0.04
0.41
−0.15
−0.12
0.05
−0.14
0.06
−0.06
406
.42

0.03
0.04
0.05
0.03
0.05
0.06
0.10

p
∗∗
∗∗∗

†
∗∗∗
∗∗∗
∗
∗∗

Self-esteemf
beta

SE

−0.01
0.06
0.11
−0.01
0.01
−0.01
0.07

0.02
0.05
0.03
0.07
0.01
0.01
0.04

p

∗∗∗

†

0.03 0.02
0.58
0.05
0.28
0.02
0.09
0.04
0.01
406
.63

0.03
0.03
0.03
0.02
0.03
0.04
0.07

∗∗∗
†
∗∗∗
∗∗

Note. Variables were centered before analyses.
aKanner, Coyne, Schaefer, & Lazarus, 1981. b Russell, Peplau, & Cutrona, 1980. c Radloff, 1977. d Watson, Clark, & Tellegen, 1988. e Adapted from Kraut &Attewell, 1997. f Heatherton & Polivy, 1991.
†
p < .10. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

Table 6. Predicting Knowledge as a Function of Use of the Internet Over Time and Individual Difference Variables, Study 2
Computer skill
Independent variables

beta

SE

Intercept
Adult (0 = teen; 1 = adult)
Male (0 = female; 1 = male)Household income
White (0 = other; 1 = White)
Education
Computer sample (0 = no; 1 = yes)
Time period (0 = 1st 6 months;
1 = 2nd 6 months)
Lagged dependent variable
Extraversion
Social support
Internet use
Internet × Extraversion
Internet × Support
Internet × Adult
n
R2

0.02
−0.11
0.05
−0.01
−0.01
0.03
−0.10
0.04

0.02
0.07
0.04
0.08
0.02
0.02
0.05
0.03

0.65
0.02
0.03
0.31
−0.02
0.10
0.14
400
.71

0.03
0.03
0.04
0.03
0.04
0.05
0.08

Note. Variables were centered before analyses.
†
p < .10. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

U.S. knowledge
p

†

∗∗∗

∗∗∗
∗

beta

SE

0.00
0.18
0.04
0.09
0.00
0.03
0.01
−0.04

0.01
0.04
0.02
0.04
0.01
0.01
0.03
0.02

0.22
−0.02
0.05
0.00
0.01
0.00
−0.01
403
.15

0.04
0.02
0.02
0.01
0.02
0.03
0.04

Local knowledge
p
∗∗∗
†
∗
∗∗∗
∗
∗∗∗
∗

beta

SE

0.00
0.13
0.04
0.06
0.00
0.03
0.02
−0.09

0.01
0.03
0.02
0.04
0.01
0.01
0.02
0.01

0.11
0.00
0.01
−0.03
0.03
0.00
0.01
403
.15

0.04
0.01
0.02
0.01
0.02
0.02
0.04

p
∗∗∗
∗

∗∗∗
∗∗∗
∗∗

∗

Internet Paradox Revisited

67

with family and friends (r = .69) and for information (r = .62) had the highest
association with the Internet use index reported in Table 2, followed by use forentertainment (r = .51) and meeting new people (r = .38). Those with more extraversion were more likely than those with less extraversion to use the Internet tokeep up with friends and family (r = .10, p < .05) and to meet new people on-line
and frequent chat rooms (r = .12, p < .05), but the associations were weak. Thosewith stronger initial social support were less likely than those with weaker support
to use the Internet to meet new people or use chat rooms on-line (r = −.11, p < .05)
or for entertainment (r = −.14, p < .05). Adults were less likely than teens to use
the Internet for meeting new people (r = −.41, p < .001) and for entertainment(r = −.29, p < .001).
To test whether particular ways of using the Internet were more beneficial
than others, we conducted a mediation analysis by adding the measures of specificInternet use to the models in Tables 3–6. These additions did not significantly affectthe interactions between overall Internet use and extraversion or social support.
Discussion
The original HomeNet sample began using the Internet in 1995 or 1996. Our
follow-up of participants remaining in the sample in 1998 showed that most of thenegative outcomes initially associated with use of the Internet dissipated, except for
its association with increased stress. The statistical interactions of loneliness anddepressive symptoms with time period suggest that use of the Internet led to negative outcomes during the first phase of the study and more positive outcomes later.
In Study 2, conducted from 1998 to 1999, more use of the Internet was associated with positive outcomes over a broad range of dependent variables measuringsocial involvement and psychological well-being: local and distant social circles,
face-to-face communication, community involvement, trust in people, positive affect, and unsurprisingly, computer skill. On the other hand, heavier Internet use
was again associated with increases in stress. In addition, it was associated withdeclines in local knowledge and declines in the desire to live in the local area,suggesting lowered commitment to the local area.
Having more social resources amplified the benefits that people got fromusing the Internet on several dependent variables. Among extraverts, using theInternet was associated with increases in community involvement and self-esteem
and declines in loneliness, negative affect, and time pressure; it was associatedwith the reverse for introverts. Similarly, among people with more rather than lesssocial support, using the Internet was associated with more family communication and greater increases in computer skill. Adults and teens gained somewhat
different benefits from Internet use, with adults more likely to increase their faceto-face interactions locally and their closeness to geographically distant relatives
and friends.

68

Kraut et al.

What accounts for the differences between the original HomeNet research,showing generally negative consequences of using the Internet, and the followups, showing generally positive consequences? Maturation of participants between
the early and late phases of Study 1, differences in samples between Studies 1
and 2, and changes in the Internet itself are all potential explanations for thisshift in results. Although our research cannot definitely choose among these
explanations, a change in the nature of the Internet is the most parsimonious
explanation.
Maturation of participants and changes in the way they used the Internet could
potentially account for the shift in results between the early and later phases ofStudy 1. For example, as the novelty of using the Internet wore off, participants
may have jettisoned unrewarding Internet activities and increased their use of
more personally rewarding ones. However, the first phase of Study 1, with its
negative outcomes, occurred during participants’ first year on-line. Study 2, with
its positive outcomes, also occurred during a 1-year period, when most participants
were new to the Internet. Thus, although maturation could account for differences
between the early and late phases of Study 1, it cannot account for differences
between Studies 1 and 2.
Participants in Studies 1 and 2 came from separate opportunity samples. Thesesample differences make comparisons between the two studies problematic andcould potentially account for differences in results between them. For example,
the original sample included a larger proportion of teens and minorities. Althoughteenagers and adults gained somewhat different benefits from using the Internet,
teenagers did not fare worse overall than adults from using the Internet. Similarly,
supplementary analyses (not shown in Tables 3–6) do not reveal racial differencesin outcomes that can account for difference between the two studies. Participants inStudy 1 had more social support and were more extraverted than those in Study 2,
probably because they were recruited from families with organizational memberships. However, the statistical interactions with extraversion and social support
reported in Study 2 would lead one to expect that outcomes would be more positive in Study 1 than Study 2, but this was not the case. Although other unmeasured
differences in the samples might account for the differences in results between
Study 1 and Study 2, differences in age, race, and social resources do not appear
to do so.
The similarity of findings comparing the early and later phases of Study 1 andcomparing Studies 1 and 2 suggests that changes in the Internet environment itself
might be more important to understanding the observed effects than maturation ordifferences between samples. Simply put, the Internet may have become a more
hospitable place over time. From 1995 to 1998, the number of Americans withaccess to the Internet at home more than quadrupled. As a result, many more ofparticipants’ close family and friends were likely to have obtained Internet access.
Similarly, the services offered on-line changed over this period, increasing theInternet Paradox Revisited

69

ease with which people could communicate with their strong ties. For example,
new communication services, such as America Online’s instant messaging, allow
users to subscribe to a list of family and friends and be notified when membersof their “buddy lists” come on-line. In addition to these changes to the on-line
social environment, over the span of this research, the Internet provided a richer
supply of information, with more news, health, financial, hobby, work, community,and consumer information available. It began to support financial and commercial
transactions. Together, these changes could have promoted better integration ofparticipants’ on-line behavior with the rest of their lives.
Our finding from Study 2 that extraverts and those with more support benefited
more from their Internet use is consistent with this idea. That is, the Internet may
be more beneficial to individuals to the extent they can leverage its opportunities
to enhance their everyday social lives. Those who are already effective in usingsocial resources in the world are likely to be well positioned to take advantage ofa powerful new technology like the Internet.
Research shows that people can form strong social bonds on-line and that
relationships formed on-line can carry over to the off-line world (e.g., Parks &Roberts, 1998; McKenna et al., this issue). However, research also suggests thatstrong relationships developed on-line are comparatively rare. Most studies showthat people use the Internet more to keep up with relationships formed off-line
than to form new ones on-line (e.g., Kraut et al., 1996; Pew Internet and American
Life Project, 2000). In addition, on-line relationships are weaker on average thanthose formed and maintained off-line (e.g., Cummings et al., in press; Gross et al.,this issue). Gross et al. (this issue) also report that adolescents who feel sociallyanxious and/or lonely are especially likely to communicate on-line with people
with whom they do not feel close. Thus one would expect that a diet filled withon-line relationships would be harmful to the social and psychological health ofInternet users. Fortunately, people don’t seem to use the Internet this way. Rather
they mingle their on-line and off-line worlds, using the Internet to keep up withpeople from their off-line lives and calling and visiting people they initially met
on-line (Kraut et al., 1996; McKenna et al., this issue).
Although the impact of using the Internet across the two studies was generally
positive, some negative outcomes remained. Across both studies, as people used
the Internet more, they reported increases in daily life stress and hassles. Supplementary analyses did not identify any single stressor that occurred more frequently
with Internet use, even though the cumulative increase with Internet use was statistically significant. One explanation is that the time spent on-line leaves less fora wide range of other activities and that this time drought may lead to a generalized
perception of stress.
In addition to increases in stress, heavier Internet use was also associated withdeclining commitment to living in the local area and less knowledge about it. These
declines may come about because the Internet makes available an abundance of70

Kraut et al.

on-line information (and social relationships) outside of the local area. Unlike
regional newspapers, for example, the Internet makes news about distant cities asaccessible as news about one’s hometown.
The mechanisms by which the Internet has its impact on social involvement
and psychological well-being remain unclear. One possibility is that the effects ofusing the Internet depend upon what people do on-line. For example, one might
expect that interpersonal communication with friends and family would have more
beneficial effects than using the Internet for downloading music, playing computer
games, or communicating with strangers. Another possibility is that all uses of theInternet are equivalent in this regard and that the important factor is not how
people use the Internet, but what they give up to spend time on-line. Thus theeffects of using the Internet might be very different if it substitutes for time spent
watching television than if it substitutes for time spent conversing with closefriends. No research to date, however, including our own, can distinguish between
these two possibilities. Our own attempts to identify the unique effects of using
the Internet for different functions were unsuccessful. Self-report measures may
be too insensitive to track true differences in use.
Understanding the mechanisms for the Internet’s impact is essential for
informing private, commercial, and public policy decisions. People need better
information to know whether to ration their time on-line or to decide whichuses of the Internet are in their long-term interests. As experience with television suggests, enjoyable uses of new technology may be harmful in the longterm (e.g., Huston et al., 1992; Putnam, 2000). Service providers need to decide what applications to offer on-line. Schools and libraries need to decide
whether to offer e-mail and chat capabilities along with their information-oriented
services.
Experiments are a standard way to assess the impact of an intervention. Although laboratory experiments can identify short-term consequences of Internet
use, they are too limited to illuminate how the Internet affects slowly emerging
phenomena, such as social relationships, community commitment, or psychological well-being (Rabby & Walther, in press). Unfortunately, it is probably too latein the evolution of the Internet to carry out true long-term experiments, at leastin North America. We tried to conduct such an experiment on Internet use forStudy 2, but in less than 12 months, 83% of the households in the control group
had acquired Internet access on their own.
Nonetheless, researchers should continue to attempt to discern how using
the Internet is affecting people’s lives with the best designs possible. Althoughcross-sectional designs are most common in research on the impact of the Internet(e.g., Cole, 2000; Parks & Roberts, 1998; Pew Internet & American Life Project,
2000; Riphagen & Kanfer, 1997), they cannot distinguish preexisting differencesamong people who use the Internet from consequences of using it. Therefore, we
believe longitudinal designs are essential to understanding the effects of Internet

Internet Paradox Revisited71

use and the differences in these effects as the Internet changes. In addition, we needbetter and more detailed descriptions of how people spend their time, both on-line
and off, to relate these detailed descriptions to changes in important domains oflife. The diary measures used by Gross et al. (this issue) are a step in this direction.

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ROBERT KRAUT is Herbert Simon Professor of Human-Computer Interaction
at Carnegie Mellon University. He holds a PhD in social psychology from Yale

Internet Paradox Revisited73

University and has worked as a research scientist at AT&T Bell Laboratories
and Bell Communications Research and as a faculty member at University ofPennsylvania and Cornell University. He has broad interests in the design andimpact of information technology. He has conducted empirical research on officeautomation and employment quality, technology and home-based employment, thecommunication needs of collaborating scientists, the design of information technology for small-group intellectual work, and the impact of national information
networks on organizations and families.
SARA KIESLER is Professor of Human-Computer Interaction at Carnegie Mellon
University. She holds a PhD in psychology from Ohio State University. Her
research examines social aspects of technology and technological change, especially computer communication and human-computer interaction. Her projects
currently span topics in human-robot interaction, use of information technology incars, home technology, impact of the Internet, electronic communities and groups,
and distributed and multidisciplinary work groups or organizations.
BONKA BONEVA is a postdoctoral fellow in the Human-Computer Interaction
Institute at Carnegie Mellon University. She received a PhD in sociology from the
University of Sofia and has done doctoral work in psychology at the University ofPittsburgh. She has previously worked at the Bulgarian Academy of Sciences, theUniversity of Sofia, Northwestern University, and the University of Pittsburgh. Her
research and publications include work on personality factors in international andinternal migration, reconstructing social identities under new sociocultural conditions, and theoretical and methodological issues of power motivation. Recently,
she has been studying the impact of gender in computer-mediated communication.
JONATHON N. CUMMINGS is a postdoctoral fellow in the Human-Computer
Interaction Institute at Carnegie Mellon University, from which he received his
PhD in organization science. He earned his BA in psychology from the Universityof Michigan and his AM in social psychology from Harvard University. He iscurrently interested in the social impact of Internet use in households and theworkplace, interaction processes and performance consequences of distributed
work, and statistical analyses and visualization tools for understanding social
networks.
VICKI HELGESON is an Associate Professor of Psychology at Carnegie Mellon
University. She received her PhD in experimental social psychology from the
University of Denver and completed a postdoctoral fellowship in health psychology
at the University of California at Los Angeles. Her research interests focus on theintersections of gender, relationships, and health. She has conducted a number of74

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longitudinal studies that focus on how people adjust to chronic illness, including
breast cancer, heart disease, prostate cancer, and diabetes. Much of this work has
been supported by the National Institutes of Health.
ANNE M. CRAWFORD is a postdoctoral fellow in the Human-Computer Interaction Institute at Carnegie Mellon University involved with longitudinal research on
the impact of computers, Internet, and other technologies on social relationships
and family interaction. Previously, she was Senior Data Analyst for the Pittsburgh
Youth Study at the University of Pittsburgh Medical Center and Health System fora longitudinal study of substance use and delinquency among adolescent males.
She was data analyst for the HIV and Alcohol Prevention Project at the Universityof Kentucky in 1998–99. She received her PhD in social psychology from the
University of Kentucky in 1998.

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